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Robotics, Automation, Artificial-intelligence and Internet-of-Things (RAAICON), 2019 IEEE International Conference on

作者： Md Shahidur Rahman Asif Ahamed Md Nuruzzaman Pranto Md Ariful Islam Abdullah Al Masum Abdullah Al-Sakib S M Arafat Rahman Rezaul Haque Shafiur Rahman Dept. of Engineering Management Westcliff University CA USA College of Technology and Engineering Westcliff University CA USA Dept. of Business Administration International American University CA USA Dept. of Information Technology Westcliff University CA USA Dept. of Computer Science and Engineering East West University Dhaka Bangladesh Dept. of Software Engineering Daffodil International University Dhaka Bangladesh

ISBN: (数字)9798331534400

ISBN: (纸本)9798331534417

Cucumber farming plays a crucial role in Bangladesh's agricultural economy, significantly contributing to vegetable production. However, diseases like Downy Mildew, Bacterial Wilt, Anthracnose, and Belly Rot threaten crop quality and lead to financial losses. Effective management of these diseases is crucial for maintaining high crop yields and ensuring economic stability. Current disease identification methods are labor-intensive, costly, and dependent on expert knowledge. To address these challenges, this study presents an automated disease categorization system using advanced transfer learning models, including MobileNet, ShuffleNet, InceptionResNet-v2, AlexNet, and Ensemble Transfer Learning (ETL). Utilizing a dataset of 8,000 well-annotated images, enhanced through preprocessing techniques like GLRM feature extraction and InfoMAX-GAN feature selection, the ETL model achieved a remarkable accuracy of 99.79% and an F1-score of 99.78%. We also developed a user-friendly web tool for real-time diagnostics, providing invaluable support to farmers and agricultural specialists. This tool enables rapid disease detection, leading to increased productivity and a transformative approach to cucumber disease management. By reducing the reliance on chemical interventions, it promotes sustainable farming practices and safeguards human and animal health.

关键词： Deep learning Accuracy Animals Biological system modeling Transfer learning Feature extraction Real-time systems Chemicals Diseases Farming

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TangiGuru: Tangible E-Learning Solution for Early Childhood Development 4

TangiGuru: Tangible E-Learning Solution for Early Childhood ...

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4th International Conference on Advancements in Computing, ICAC 2022

作者： Hettiarachchi, Thiwanka Cholitha Semasinghe, Lakisuru Sathyajith Lokuliyana, Shashika Gamage, Narmada de Silva, Rajitha Dept. Computer Systems Engineering Sri Lanka Institute of Information Technology Sri Lanka Lincoln Institute for Agri-Food Technology University of Lincoln United Kingdom

ISBN: (纸本)9798350398090

Exploration and manipulation of physical objects are essential for early childhood learning. Previous investigations found several TUI uses in other fields. Less research has been done on tangible learning for youngsters;thus, it is unclear if they are more collaborative, playful, or functional. TangiGuru consists of 12 tangible, manipulative objects known as TangiCubes, which are used as a tangible user interface between children and the e-Learning application. It can carry out cognitive learning activities related to colors, languages, shapes and basic math by dynamically varying assigned values by changing the external appearance of TangiCubes. This dynamic nature of the Tang- iCubes makes it possible to use the same tangibles with endless possibilities compared to traditional tangible learning solutions with static value for each tangible. After the prototyping phase, children were evaluated with the traditional tangible learning solutions compared to TangiGuru. They concluded that the more interactive tangible interfaces could make the children perform activities more engagingly. © 2022 IEEE.

关键词： E-learning

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Maximizing Reachability in Factored MDPs via Near-Optimal Clustering with Applications to Control of Multi-Agent systems

Maximizing Reachability in Factored MDPs via Near-Optimal Cl...

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IEEE Conference on Decision and Control

作者： Carmel Fiscko Soummya Kar Bruno Sinopoli Dept. of Electrical and Systems Engineering Washington University St. Louis MO Dept. of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA

We consider cluster-based control of agents mod-eled as a transition-independent Markov decision process (MDP), and the objective of assigning agents to clusters to maximize the size of the reachable state space. This goal is relevant to applications for which the same MDP model may be used to compute policies for different reward functions. The system controller wishes to define clusters to maximize flexibility within the attainable outcomes. Under the transition-independent MDP formulation, we first show that the size of the reachable state space is a submodular function. While maximizing the reachable state space subject to a desired number of clusters is a hard problem, properties of submodular optimization can be leveraged to propose approximate clustering techniques. We next demonstrate that a greedy clustering approach is a viable approximate solution and has a bounded optimality gap. We compare the performance in terms of value and computation complexity in using the flexibility-optimized clustering assignment versus a clustering assignment optimized for a specific reward function; there will be a loss in value at a savings in complexity. Finally, we demonstrate the utility of the flexibility-optimized clustering assignment in simulation on the same MDP model with various reward functions.

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Eggplant Disease Diagnosis Using a Robust Ensemble of Transfer Learning Architectures

Eggplant Disease Diagnosis Using a Robust Ensemble of Transf...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Md Ismail Hossain Siddiqui Zishad Hossain Limon Shakil Khan Mahbub Alam Khan Hamdadur Rahman Shimanto Krishna Chakroborty Shamim Forhad Abdullah Al-Sakib S M Arafat Rahman Shafiur Rahman Dept. of Engineering/Industrial Management Westcliff University Irvine CA USA Dept. of Computer Science Westcliff University Irvine CA USA Dept. of Business Analytics International American University CA USA Dept. of Management Information System Pacific State University USA Dept. of Management of Information System International American University USA Dept. of Computer Science and Engineering East West University Dhaka Bangladesh Dept. of Electrical and Electronics Engineering American International University Bangladesh Dept. of Information Technology Westcliff University USA Dept. of Software Engineering Daffodil International University Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Eggplant diseases present major challenges to agricultural productivity, leading to yield losses and threatening farmers livelihoods. Traditional diagnostic methods tend to be resource-intensive, subjective, and often inaccurate, making them unsuitable for large-scale use. This study addresses these shortcomings by proposing an Ensemble Transfer Learning (ETL) approach for precise classification of eggplant diseases. The model combines the strengths of ConvNeXt, EfficientNetV2, NFNet, and RegNet architectures, enhancing generalization and minimizing overfitting. Two datasets containing over 19,800 images were utilized for training and validation, with advanced preprocessing and data augmentation techniques applied to ensure high input data quality and model robustness. To assess performance, evaluation metrics such as accuracy, F1-score, MCC, and PR-AUC were used. The model achieved an impressive classification accuracy of 99.76%, surpassing state-of-the-art methods and demonstrating strong generalization across various conditions. A web-based application was also developed, enabling real-time disease diagnostics through a user-friendly interface. This application provides farmers with actionable insights, promoting sustainable farming practices by reducing crop losses and minimizing the need for chemical interventions. However, its reliance on high-quality labeled images limits its applicability in uncontrolled environments. Future research will aim to integrate more diverse datasets and develop mobile-based solutions to enhance scalability and usability. This study emphasizes the potential of ensemble transfer learning to transform agricultural disease diagnosis and improve crop productivity.

关键词： Productivity Accuracy Transfer learning Crops computer architecture Real-time systems Medical diagnosis Chemicals Diseases Farming

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Zero-Day Malware Detection through Unsupervised Deep Learning 3

Zero-Day Malware Detection through Unsupervised Deep Learnin...

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3rd International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2023

作者： Mousa, Mohammad Bahaa-Eldin, Ayman M. Sobh, Mohamed Taha, Ayman Ain Shams University Faculty of Engineering Computers and Systems Eng. Dept. Cairo Egypt Misr International University Ain Shams University Cairo Egypt Misr International University Faculty of Computer Science Cairo Egypt

ISBN: (纸本)9798350306231

This paper investigates the problem of zero-day malicious software (Malware) detection through unsupervised deep learning. We built a sequence-to-sequence auto-encoder model for learning the behavior of normal software by monitoring the sequence of operating system kernel API calls. After training is completed, the model is used to identify malicious software activities through assigning an anomaly score for each testing sample. The model achieves 90% AUC score compared to 86% AUC score for the previous work done over the same dataset used for performance assessment. © 2023 IEEE.

关键词： Malware

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Rethinking Machine Ethics – Can LLMs Perform Moral Reasoning through the Lens of Moral Theories?

arXiv

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arXiv 2023年

作者： Zhou, Jingyan Hu, Minda Li, Junan Zhang, Xiaoying Wu, Xixin King, Irwin Meng, Helen Dept. of Systems Engineering & Engineering Management The Chinese University of Hong Kong Hong Kong Dept. of Computer Science & Engineering The Chinese University of Hong Kong Hong Kong

Making moral judgments is an essential step toward developing ethical AI systems. Prevalent approaches are mostly implemented in a bottom-up manner, which uses a large set of annotated data to train models based on crowd-sourced opinions about morality. These approaches have been criticized for overgeneralizing the moral stances of a limited group of annotators and lacking explainability. This work proposes a flexible top-down framework to steer (Large) Language Models (LMs) to perform moral reasoning with well-established moral theories from interdisciplinary research. The theory-guided top-down framework can incorporate various moral theories. Our experiments demonstrate the effectiveness of the proposed framework on datasets derived from moral theories. Furthermore, we show the alignment between different moral theories and existing morality datasets. Our analysis exhibits the potential and flaws in existing resources (models and datasets) in developing explainable moral judgment-making systems. © 2023, CC BY-NC-ND.

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Common Object Request Broker-based Publisher-Subscriber Middleware for Internet of Things - Edge Computing

Common Object Request Broker-based Publisher-Subscriber Midd...

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2022 International Research Conference on Smart Computing and systems engineering, SCSE 2022

作者： Perera, Hansani Jayakody, Anuradha Sri Lanka Institute of Information Technology Dept. of Computer Systems Engineering Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Sri Lanka

ISBN: (数字)9781665473750

ISBN: (纸本)9781665473750

The edge computing layer in IoT reduces the flow of a massive amount of data directly to the cloud by processing some data in the local network. The middleware in the layer enables this processing of data and the communication between heterogeneous devices and services in the nearby layers. CORBA, which uses as a powerful middleware technology in developing middleware solutions in enterprise-level distributed applications, has been abandoned in the current generation. The paper presents the design, and the performance evaluation of a publisher-subscriber middleware implemented using CORBA that was studied when exploring the applicability of CORBA as an IoT edge computing middleware. The evaluation was continued in two steps to analyse several parallel connections (Load test) and handle requests in a unit time (burst test) via simulating an IoT environment in a cloud environment. © 2022 IEEE.

关键词： Middleware

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A novel approach incorporating feature extraction followed by YOLOv7 for quality enhancement of mangoes in Bangladesh

A novel approach incorporating feature extraction followed b...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Debashis Kundu Md. Mushfiqur Rahaman Md. Nazmus Sakib Md. Faruk Abdullah Al Sohan Jannatul Fardaush Mithu Afzal Hossain Nishita Aktar Dept. Management Information Systems American International University- Bangladesh Dhaka Bangladesh Dept. of Computer Science and Engineering Daffodil Institute of IT Dhaka Bangladesh Dept. of Computer Science and Engineering World University of Bangladesh Dhaka Bangladesh Dept. of Computer Science American International University- Bangladesh Dhaka Bangladesh Dept. of Electrical and Computer Engineering North South University Dhaka Bangladesh Dept. of Computer Science and Engineering Army Institute of Business Administration (AIBA) Savar Dhaka Bangladesh

Qualitative approach for automated grading and quality assessment of fruits, machine learning techniques are crucial in agricultural applications. Automation enhances a nation’s agricultural quality, production, and economic prosperity. Fruit quality grading, particularly the surface fault identification of a fruit, is a crucial indicator in the export market. This is particularly important for mangoes, which are quite well-liked inBangladesh. On the other hand, the physical grading of mangoes is a procedure that is labor-intensive, prone to error, and very subjective. In this paper, we proposed a YOLOv7 integrated Discrete wave transformation computer vision system. The proposed model includes support vector machine (SVM) and decision tree for the classification of high-quality mangoes. The results of the experiments show that the proposed solution obtained 96.25% accuracy when the system was trained and tested using a publicly accessible mango database.

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Credit Card Fraud Detection Using Synthetic Minority Oversampling Technique and Deep Learning Technique

Credit Card Fraud Detection Using Synthetic Minority Oversam...

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International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)

作者： Abdullah M. Elmangoush Hanein O. Hassan Ayyah A. Fadhl Malak Ahmed Alshrif dept. of computer networks Libyan International University Benghazi Libya dept. of software engineering Libyan International University Benghazi Libya dept. of artificial intelligent Libyan International University Benghazi Libya dept. of telecommunication Libyan International University Benghazi Libya

ISBN: (数字)9798350351484

ISBN: (纸本)9798350351491

recently, credit card frauds are raised to huge figures due to the reliance on online shopping by users. E-commerce and many other online sites have increased online payment modes which increased the risk of online fraud. There are many techniques have been used by researchers to protect against and detect credit card fraud. Different machine-learning algorithms were used to detect and analyze fraud in online transactions. However, existing solutions suffer from two main issues: class imbalance and insufficient feature extraction methods. Furthermore, extracting relevant features for fraud detection need human-based features engineering which time-consuming and complex problem. The aim of this study is to design and develop an effective credit card fraud detection model. The class imbalance problem has been addressed using a synthetic minority oversampling algorithm. Then, using the sequential deep learning techniques, a credit card detection model was designed and developed to utilize the data generated from SMOTE to improve the feature extraction and representation to solve the problem of insufficient features. The performance of the proposed model has been evaluated by comparing it with the related work in terms of accuracy, detection rate, and f-measure. Results show that the proposed model outperforms the existing state of the arts related models. It achieves a 0.99924 Accuracy, and 0.75976 F-measure. The proposed credit card fraud detection model, which addresses the class imbalance problem using synthetic minority oversampling and utilizes sequential deep learning techniques for improved feature extraction and representation, has shown to outperform existing models in terms of accuracy, detection rate, and f-measure. This could potentially have a significant impact on online markets as it may lead to a decrease in credit card fraud, which would increase consumer trust and confidence in online transactions. Additionally, the proposed model’s improved feature e

关键词： Deep learning Accuracy Costs Credit cards Feature extraction Data models Fraud

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Enhancing Communication through Speech to Sign Language Converter

Enhancing Communication through Speech to Sign Language Conv...

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North Karnataka Subsection Flagship International Conference (NKCon), IEEE

作者： Gayana M N Mohammed Rumaiz Shaz Rasheed Hussain Shravya A Shreenath Acharya Sana Parveen Dept. of Intelligent Computing & Business Systems St Joseph Engineering College Mangaluru India Dept. of Computer Science and Engineering St Joseph Engineering College Mangaluru India

There are millions of people suffering from speaking and hearing disabilities. Sign language is the only way to communicate for such people to convey their message. The advancement in technology has highly impacted this section of society, hence there is a need to deal with them. This paper describes the Application, "Speech to Sign Language Converter" for communicating with people having hearing and speaking impairments. The primary function of the Application is to convert input sentences into Indian Sign Language (ISL) actions in real time. It takes input in the form of speech or text and produces corresponding sign language using an Avatar which acts based on SiGML code. It can also be used to convert PDF based texts into sign language. In addition, the users can learn Indian Sign Language (ISL) using the tutorials provided within it. The application focuses mainly on converting local languages such as Kannada into ISL.

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